Recent efforts have focused on natural language questions as an interface for KBs, where questions are translated to structured queries via semantic parsing (Bao et al., 2016;Bast and Haussmann, 2015;Fader et al., 2013;Mohammed et al., 2018;Reddy et al., 2014;Yang et al., 2014;Yao and Durme, 2014;Yahya et al., 2013).

we do not pair ComQA with a specific knowledge base (KB) or text corpus for answering.


The sophistication of the linguistic structure of the questions in the FreebaseQA data set is compared to other similar data sets based on the average length, in number of words, of the questions.

machine learning approaches for NLP are data hungry since they require large amounts of real-world data to train the models for the best possible performance.


Targeting on the two main steps, subgraph selection and fact selection, the research community has developed sophisticated approaches.

the reason is that, as opposed to conventional methods which rank the entire subgraph returned from unigram matching to select the top-n candidates, we choose only the first 200 candidates from the subgraph and then rank them with our proposed ranking score.


Table 13 shows two major types of error, where the correct answer choice is in bold and the predicted answer choice is in italics.

in this paper, we study question answering on the Ai2 Reasoning Challenge (ARC) scientific QA dataset .


MetaQA is a multihop dataset for end-to-end KBQA based on a movie knowledge graph with 43k entities.

we select it to demonstrate that UHop works for long as well as task-specific relations.


The current framework uses a greedy search for each single hop.

the number of hops is generally restricted to two or three.


To evaluate if the model halts in the search process, we conducted an experiment using PQL3 as the training/validation set and PQL2 as the testing set.

the performance on 3-hop data suffers when trained on 2-hop data.


Then contextual-level feature is used to offset the deficiency of GLoVe.

as shown in Table 1, we collected three kinds of results.


In many scenarios, we need to comprehend the relationships of entities across documents before answering questions.


Although Coref-GRU extends GRU with coreference relationships, it is still not enough for multi-hop because hop relationships are not limited to coreference, entities with the same strings also existed across documents which can be used for reasoning.

our BAG model achieves the best performance under all data configurations.


k = 10, the VLAWE document representation can grow up to thousands of features, as the number of features is k • d, where d = 300 is the dimensionality of commonly used word embeddings.

our experiments on five benchmark data sets prove that our approach yields competitive results with respect to the state-of-the-art methods.


Our approach is inspired by the Vector of Locally-Aggregated Descriptors used for image representation, and it works as follows.

vLAWE is robust to vocabulary distribution gaps between training and test, which can appear when the training set is particularly smaller or from a different domain.


System (Poria et al., 2017b) uses contextual information for the prediction but without any attention mechanism.

anger, disgust, happy and sad) for the same utterance.


For example, 'text' carries semantic information of the spoken sentence, whereas 'acoustic' information reveals the emphasis (pitch, voice quality) on each word.

similarly, the textual representation of second example "I'm fine.'


Why is the experimental result of the BERT-pair model so much better?


Following the setup of (Hsu and Ku, 2018), in Friends and EmotionPush, we only evaluate the model performance on four emotions: anger, joy, sadness, and neutral, and we exclude the contribution of the rest emotion classes during training by setting their loss weights to zero.

(b) The two variants, HiGRU-f and HiGRU-sf, can further attain +0.9 and +1.5 improvement over Hi-GRU in terms of WA and +1.0 and +1.2 improvement over HiGRU in terms of UWA, respectively.


For example, people are usually calm and present a neutral emotion while only in some particular situations, they express strong emotions, like anger or fear.

these models treat texts independently thus cannot capture the inter-dependence of utterances in dialogues (Kim, 2014;Lai et al., 2015;Grave et al., 2017;Chen et al., 2016;Yang et al., 2016).


The performance of the sadness emotion is significantly boosted and that on the anger emotion is at least unaffected.

the transcripts do not reveal very strong emotions compared to what the characters might act in the TV show.


We find that our joint neural approach to SCL improves unsupervised domain adaptation substantially on a standard sentiment classification task.

the key idea in SCL is that a subset of features, believed to be predictive across domains, are selected as pivot features.


Our results also show that while existing pivot selection methods perform well, they are below an oracle-provided ceiling for many source-target pairs for the sentiment classification task we examine.

a neural version of SCL still obtains near state-of-the-art performance (Ziser and Reichart, 2017).


In the unsupervised case, such a dictionary can be induced from the monolingual embeddings S and T (Artetxe et al., 2018).

unsupervised BWE methods learn such a mapping without any parallel data.


We introduce a new d-dimension vector a p ∈ O = {x ∈ R d | x ≤ 1} to represent the "positive direction", which is to be learned.

we iterates over the following three steps: 1.


Following (Artetxe et al., 2018), we first compute the similarity matrices TT , sort them along the second axis and normalize the rows, yielding M s and M t .

our objective is convex with respect to either W s or a p , thus can be efficiently minimized by using the projected gradient descent algorithm.


We have also explored the use of sub-word units learned with byte pair encoding (BPE) (Sennrich et al., 2016).

for reasons of space, we report only one example in Table 3, but more examples are available in the supplementary material.


In the example, the baseline has chosen a generic word, "program", while ReWE has been capable of correctly predicting "Default Program" and being specific about the object, "it".

as * * The author has changed affiliation to Microsoft after the completion of this work.


By applying the two sampling steps described in Section 3.1, about 10M and 6M augmented Ch-En and En-Ru sentences are generated, respectively.

h i,j is calculated as follows: First, a self-attention sub-layer is employed to encode the context.


The sentences are extracted from e-commerce websites, in which "subject"s are the goods names shown on a listing page.

to their methods, our method does not make changes to the decoder, and therefore decoding speed remains unchanged.


7 In both languages, we set the number of clusters to 5000 (a hyperparameter in the algorithm, c = 5000), that is, we will obtain trees with 5000 leaves.


Except for layer 1, it is also evident to see larger gaps (more than 20%) at lower layers than higher layers due to the fact that lower layers, which are distant from the topmost loss in the baseline, require more supervision signals to shape their latent representation space.

it is impossible to evaluate the hidden representation on all those tasks; moreover, due to relationship between tokens (Hu et al., 2016) in Y, not all partitions are reasonable.


And more similar tasks have closer performances.

we can obtain 4 tasks with s(l) = 5, 8, 11, 20 for the Zh⇒En task and s(l) = 5, 7, 10, 21 for the En⇒De task, where l = 2, 3, 4, 5 of the 6-layer decoder.


et al., 2018b), and text-to-text generation evaluation (Birch et al., 2016;Choshen and Abend, 2018;Sulem et al., 2018a).

tUPA often succeeds at making distinctions that are not even encoded in UD.


The latter can be achieved using a model trained to reproduce (or mimic) the original embeddings (Pinter et al., 2017).

we then train Mimick (Pinter et al., 2017) as well as both FCM and AM on the skipgram embeddings.


We then define the reliability of a context as where is a normalization constant, ensuring that all weights sum to one.

embedding methods generally need many observations of a word to learn a good representation for it.


For a comprehensive comparison, it is ideal to have plots for all models.

it identifies and removes style words from texts, searches for related words pertaining to a new target style, and combines the de-stylized text with the search results using a neural model.


One reason for this is a lack of suitable evaluation resources.

we used the comparative annotation technique Best-worst Scaling, which addresses the limitations of traditional rating scales.


Bigrams (two-word sequences) are especially important there since they are the smallest unit formed by composing words.

the high average relatedness and low standard deviation ( ) for the transpose bigrams, indicate that these pairs tend to be closely related to each other.


Observe that among the three methods of word vector representations, the best results are obtained using fastText (word-context matrix factorization model being a close second).

semantically related concepts may not have many properties in common, but there exists some relationship between them which lends them the property of being semantically close.


Identifying outliers can lead to better datasets by (1) removing noise in datasets and (2) guiding collection of additional data to fill gaps.

to demonstrate this idea, we developed a novel crowdsourcing pipeline for data collection.


One way to better satisfy user intents is by making such processes collaborative (Morris and Horvitz, 2007;Morris, 2013), or conversational (Radlinski and Craswell, 2017).

which advice-seeking question is more likely to have been asked by the narrator: Q1: Is it even possible to be addicted to coffee?


In all of the following evaluations, the systems are given the two pools of perspectives U p and evidences U e .

we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim.


Modeling how visual and linguistic information can jointly contribute to coherent and effective communication is a longstanding open problem with implications across cognitive science.

subjects' intuitions suggest that coherent imagery typically does not contribute instruction content, but rather serves as a visual signal that facilitates inferences that have to be made to carry out the instruction regardless.


A crucial limitation to all these approaches lies in the modeling of appropriate historical context, which is simply ignored in most of the works.

first, we use a gating mechanism based on c s and c u that determines the relevance of the previous system utterance in the current turn.


We develop CLEVR-Dialog, a large diagnostic dataset for studying multi-round reasoning in visual dialog.

we hope the findings from CLEVR-Dialog will help inform the development of future models for visual dialog.


To learn embeddings for verbs and arguments, we extract representations for sentences containing only the word itself.

at the recent VU amsterdam (VUa) metaphor identification shared task (Leong et al., 2018), neural approaches dominated, with most teams using LSTMs trained on word embeddings and additional linguistic features, such as semantic classes and part of speech tags (Wu et al., 2018;Stemle and Onysko, 2018;Mykowiecka et al., 2018;Swarnkar and Singh, 2018).


deflate economy) (Rei et al., 2017) or the sentence containing an utterance (Gao et al., 2018).

we concatenate these features into a single feature vector and feed them into a gradient boosting decision tree classifier (Chen and Guestrin, 2016).


Table 5 further presents examples requiring at least paragraph-level context, along with gold label and model predictions.

we also observed 4 errors (13%) due to examples with non-verbs and incomplete sentences and 11 examples (35%) where not even paragraph-level context was sufficient for interpretation, mostly in the Conversation genre, demonstrating the subjective and borderline nature of many of the annotations.


For example, the word embedding they use (word2vec embedding trained on the Google News dataset 1 (Mikolov et al., 2013)) an-1 https://code.google.com/archive/p/word2vec/ swer the analogy "man is to computer programmer as woman is to x" with "x = homemaker".

if one cannot determine the gender association of a word by looking at its projection on any gendered pair.


However, this is not possible when projecting multiple classes into a linear space.

we can observe bias in word embeddings in many different ways.


We conducted experiments to verify our method on a benchmark MPQA dataset.


For each set of classes, we consider a setting where directed relations are predicted with one where the direction is ignored.

here, we use a discrete emotion categorization scheme based on fundamental emotions as proposed by Plutchik.


Full-match evaluation adds nothing to F 1 @5 and F 1 @7 scores.

the system extracts profiles of researchers from digital resources and integrates their data in a common network.


3) of the compressor at t = M + 1 and onward, with the one-hot distribution of the EOS token.

all the RNNs are LSTMs (Hochreiter and Schmidhuber, 1997).


Measuring the performance of a summarization system can be done through either automatic or manual evaluation.

in the SCU dataset, we mark the SCUs we used in our experiments, including their grouping as tasks in the system evaluation phase.


3 We test the naive LSH-based partitioning (LSH-only)  Reference  4029 3h 12m 32s  1847 24m 21s 10131 22h 48m 08s   LSH-only  3694  1s  1752  1s  7827  2s  LSH-CW  4085  23s  1875  11s  9861  58s   Table 1: Concept mention grouping runtimes on average and for the smallest and largest set.

since comparing neighbors in a sorted list of bit hashes will primarily find those that differ in the last positions, the random permutations are the key part of the algorithm that ensures similar hashes differing at varying positions are found.


As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it.

that directly leads to a simple O(n) grouping method.


7 The original AllenNLP library uses a byte representation.


this can be easily represented as a loop that traverses the input sentence from left to right, linking each word to another from the same sentence or to the dummy root.


We gratefully acknowledge NVIDIA Corporation for the donation of a GTX Titan X GPU.

we keep the model that obtains the highest UAS on the development set.


When the proper assumptions on the available labels are made, one can typically model the missing labels as latent variables and train a latentvariable conditional random fields model (Quattoni et al., 2005).

due to their uniform assumption on q over the missing labels, these models typically can recall more entities.


Their work focused on the citation parsing 3 (i.e., sequence labeling) task which does not suffer from the above issue as no O label is involved.

the model regards the missing labels as latent variables and learns a latent variable CRF using the following loss: the resulting model is called missing label linear-chain CRF (M-CRF) 6 .


Actually, "died" and "fired" are the trigger words of Death and Attack events, respectively.

the results of baseline systems are listed in the first group.


Building on the baseline above, we establish a new architecture that is able to capture the sub-event types as well as their duration.

we (i) establish a neural baseline that outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7% micro-F 1 improvement), as well as (ii) demonstrate superiority of a recurrent neural network model on the posts sequence level for labeled sub-events (2.4% bin-level F 1 improvement over non-sequential models).


The first one, referred to as relaxed evaluation, is commonly used in entity classification tasks (Adel and Schütze, 2017;Bekoulis et al., 2018a,c) and similar to the binary classification baseline system evaluation: score a multibin sub-event as correct if at least one of its comprising bin types (e.g., goal) is correct, assuming that the boundaries are given.

in this work, we propose to use the bin-level evaluation, since it is a more natural way to measure the duration of a sub-event in a supervised sequence labeling setting.


Trivial feature augmentation also does not work well, confirming the necessity of learning the graph embedding with GCN.

miwa and Bansal (2016) applied Tree LSTm (Tai et al., 2015) to jointly represent sequences and dependency trees for entity and relation extraction.


Hyper-parameters: In our experiments, we use 25 dimensional embedding vectors for the Rowless model, and 12 dimensional embedding vectors for the E-and ENE models.

figure 1 shows the architecture of our model.


(2015) exploit graph information for knowledge base completion.

this also makes the model overfit on our data as the number of OpenIE text predicates observed with each entity pair is rather small (1.4 on average in our datasets).


This suggests that M itself provides important information for recognizing entity types.

the corresponding PR curves can be found in the appendix ( Figure 5).


As these type vectors are independent, the label correlations are only implicitly captured by sharing the model parameters that are used to extract f .

it achieves a 15.3% relative F1 improvement and also less inconsistency in the outputs.


A.1 Performance on the whole testing data over time The performance on the whole testing data over time is shown in Figure 3.

then we apply K-Means (the number of clusters equals the budget given to the specific task) to cluster all the samples of the task.


Practically, with the growth of the number of tasks, it is difficult to store all the task data 1 .

we improve the basic EMR with two motivations: (1) previous lifelong learning approaches work on the parameter space.


Therefore, in lifelong learning research, the learner is usually constrained on the memory size, denoted as a constant B.

we also show that the EMR outperforms GEM on many benchmarks, suggesting that it is likely to be among the top-performed lifelong learning algorithms, and it should never be ignored for comparison when developing new lifelong learning algorithms.


• We propose an alignment model which aims to alleviate the catastrophic forgetting problem by slowing down the fast changes in the embedding space for lifelong learning.

they do not suit many lifelong settings in NLP.


Moreover, sequence models such as LSTM cannot be applied to such long sequences.

this type of approach tends not to work well for NLP applications as the semantic concepts/classes in NLP are often more complex and cannot be easily described by a set of pre-defined attributes.


This decrease is likely due to the fact that AIDA-light was last updated in 2014 while the CNN/DailyMail datasets contain articles collected until the end of April 2015.

the default approach we use is to concatenate the claim and context into a linear sequence of tokens during preprocessing (shown in Figure 4a).


For the growth of any company or application it is necessary for the customer care agents to be cordial and amicable to the customer.

our reward function r(y), used for evaluating y against the gold standard output is It is the weighted mean of the two terms: (i) BLEU metric m1: Ensures the content matching between the reference and the decoded outputs.


The second layer leverages the external knowledge to compute F k , which consists of pair-wise knowledge score f k among all candidate n. To enhance the efficiency of the model, a softmax pruning module is applied to select high confident candidates into the second layer.

moreover, a softmax pruning is placed in between the two layers to select high confident candidates.


Several baselines are compared in this work.

the first layer encodes the contextual information for computing F c .


For this knowledge, we create a knowledge base by counting how many times a predicate-argument tuple appears in a corpus and use the resulted number to represent the preference strength.

for End2end, we use their released code 13 and replace its mention detection component with gold mentions for the fair comparison.


The second application of t 4 has n−1 choices of q, and the ith application of t 4 has n − (i − 1) choices.

different ways to consume the q states, each producing a unique DAG.


(2015); Leviant and Reichart (2015); Mrkšić et al.

an example output for all three methods is shown in Table 1.


We propose a general framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components, also including more advanced techniques based on position embeddings and self-attention.

sms is less useful for entity typing, where almost all best-performing configurations are based on morf (see also Figure 4).


We notice that Sent2Vec trigrams model dominates the word-similarity tasks as well as the semantic analogy tasks.

the reported results are given as mean and standard deviation for those five models.


We show that the addition of MIL regularizers for generating explanations using thresholded attention improved precision and recall hypothesis explanations.

this is achieved by penalizing the smallest nonzero attention weight, which has the effect of encouraging at least one weight to be close to zero.


This behaviour is also more pronounced in the premise sentence highlights rather than the hypothesis.

similar improvements were not realized for the premise sentence.


Furthermore, the mean score for our system (79.2) is close to the mean of the best performing models (81.0), which are different systems, while using simpler features and learning algorithm.

in this paper, we are interested in both monolingual and cross-lingual CWi; in the latter, we build models to make predictions for languages not seen during training.


The embedding based approaches suffer from accumulation of the embedding and training error (Balasubramanian and Lebanon, 2012), however in the proposed approach, we have removed the embedding step and considered the training error minimization at the label subset selection step.

in (Bhatia et al., 2015b), the authors perform local embedding of the label vectors.


The content of web pages was represented using the Boolean bag-ofwords model.

for Bibtex datasset, our proposed method is competitive with the best results, and for Eurlex and Wiki10-31k, our method is substantially better than both SLEEC and FastXML, a notable achievement for a single model approach.


• Relation extraction within instances is equal to the query "what is the relation between head and tail at time spot t i ?".

suppose we are to predict their relation after their divorce.


The models in previous work (Zeng et al., 2015;Lin et al., 2016;Luo et al., 2017) generally include two parts, encoding and fusion.

for NYT-10 experiments, we adopt a single query without temporal encoding to compare results with other baseline methods since the dataset only contains one label for each mention set.


Experiments and results, which demonstrate the performance of our framework, are presented in section 3.

our proposed framework leverages not only the word embeddings but also other semantic knowledge.


In particular, given the wordpair (w, w ), and their provided contexts (c, c ) we define: Following the notation used in 3.2, K is the number of topics returned by the trained LDA model, x j is the word embedding trained on the subcorpus corresponding to the j-th topic after being projected to the unified vector space, p(j|w, c) denotes the posterior probability of topic j returned by LDA given as input the context c of word w, d denotes the cosine similarity between the two input representations and finallyx (w) = u argmax 1≤j≤K p(j|w,c) (w) is the vector representation of word w that corresponds to the topic with the maximum posterior for c. Intuitively, a higher score in MaxSimC indicates the existence of more robust multi-topic word representations.

the topic model splits the corpus into K (possibly overlapping) subcorpora.


Afterward, following a soft clustering scheme each sentence is included in a topic-specific corpus when the posterior probability for the corresponding topic exceeds a predefined threshold.

this method is restricted to languages where such lexical resources exist and depends on the lexical coverage and quality of such resources.


Interestingly, for these points the predicted scores for the baseline word vectors were nearly correct, and retrofitting pushed them to overpredict similarity.


w rand is a random token in the sentence that is not the head of w mod .

we also see that the OpenAI transformer significantly underperforms the ELMo models and BERT.


While it may be surprising that GloVe initialization is harmful, it is well known that pretrained word embeddings do not necessarily capture syntactic relationships (as evident in the poor performance of k-means clustering).

given random word y ∈ V , we set x ∈ V 2H to be an ordered list of H left and H right words of y.


Thus we focus on the numerator term By the product rule, its gradient with respect to q is a sum of two terms.

x N ), it assumes a uniform distribution over consecutive word pairs (x i−1 , x i ) and optimizes the following empirical objective where #(c, c ) denotes the number of occurrences of the cluster pair (c, c ) under C. While this optimization is intractable, Brown et al.


Each objective involves entropy estimation which is a nonlinear function of all data and does not decompose over individual instances.


This in turn means that J var = H(Z) − H(q, p) is a lower bound on I(X, Z), hence the name.

the first term is (using (10) again) the second term is (as a sum over batches) In contrast, the numerator term estimated as an average over minibatches is and the two terms of its gradient with respect to q (corresponding to (12) and 13  the difference between (12) and 14is where the difference between (13) and (15) is Adding these differences gives the second result.


2018, whose hyperparameters were tuned by us.

in the unsupervised case, the standard approach is to maximize the log marginal likelihood, this summation is intractable because z t fully depends on all previous actions [z 1 , .


We also look at ELMo's output when given the entire sentence.

the formula for the compatibility function (and its normalized form e) is defined as follows: Where the bilinear projection φ is defined as: For the composition function a we used either a treeLStM (tai et al., 2015) or a 2-layer MLP (see Appendix A.2 for more precise definitions on both methods).


It contains text and named entity labels in English, Spanish, German and Dutch.

the table lists the F1 score for each entity types, as well as the overall F1 score.


We can observe a noticeable gap between unsupervised and supervised methods, but the gap is narrowing as the rank increases.

unsupervised NMT The current NMT systems (Sutskever et al., 2014;Cho et al., 2014a;Bahdanau et al., 2015;Gehring et al., 2017;Vaswani et al., 2017) are known to easily overfit and result in an inferior performance when the training data is limited (Koehn and Knowles, 2017;Isabelle et al., 2017;Sennrich, 2017).


Although the M may contain potential parallel sentences t for s, we cannot directly use (s, t ) as ground-truth sentence pairs to train the translation model V s→t because the NMT system is sensitive to noises (Cho et al., 2014a;Cheng et al., 2018).

note that (1) all the encoders share the same parameters (same for decoders); (2) the decoding processes are nondifferentiable, so the language modeling loss and the comparative translation loss are used to train the learning modules before and after the decoding processes, respectively.


The Effect of Extraction Number k As shown in Table 1, the number k of the extracted-andedited sentences plays a vital role in our approach.

recent neural-based methods (Chu et al., 2016;Grover and Mitra, 2017;Grégoire and Langlais, 2018) learn to identify parallel sentences in the semantic spaces.


Based on the semantic information of the source sentence s, we can further improve the extracted results with this editing mechanism.

owing to the shared encoders and decoders in language modeling, the semantic spaces of two languages are already strongly connected in our scenario.


( , 2019 and  propose to aggregate the multi-layer representations, and Dehghani et al.

especially, among these probing tasks, 'TrDep' and 'Toco' tasks are related to syntactic structure modeling.


Figure 4 shows the LCR curves for MultiWoz, with a trend similar to the previous section: the word-level models can only achieve task reward improvement by sacrificing their response decoder PPL.

dealOrNodeal is a negotiation dataset that contains 5805 dialogs based on 2236 unique scenarios .


The basic idea is to use an ROC-style curve to visualize the tradeoff between achieving higher reward and being faithful to human language.

(2) to our best knowledge, our work is the first comprehensive study of the use of latent variables for RL policy optimization in dialog systems.


Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge.

the word-level RL's success rate also improves to 79%, but the generated responses completely deviate from natural language, increasing perplexity from 3.98 to 17.11 and dropping BLEU from 18.9 to 1.4.


Now as we move along each direction, we can see our model gradually transforms the response toward the corresponding responses of each direction.

this essentially makes the inference process simple and robust in that one can choose arbitrary directions to generate diverse responses.


• It's a it's a luxury • Well that's interesting.

re-ranking can be extremely 1 An implementation of our model is available at https: //github.com/golsun/SpaceFusion 2 For simplicity, we omitted the response at the center: "I would love to play this game".


(Choi et al., 2018) 10.0m 93.1 86.0 600D Residual stacked enc.

given a sentence, the goal of SRL is to identify the arguments of each target verb into semantic roles, which can benefit many downstream NLP tasks.


6 The annotators were not provided with knowledge from any external lexical resource (such as WordNet).

we used two simple binary classifiers in our experiments on top of all comparison systems (except for the LSTM baseline).


• (2.7) Our main contribution is introduction of ### without requiring neither supervision nor feature engineering.

we do so in order to analyze and disentangle the sources of review updates during the rebuttal stage.


• We conduct t-test and get the p value as ###, which shows good agreement.

• (2.6) In response to your general remark: we can see how our discussion and conclusions would lead a reader to conclude that; rather, this paper is an exploration in an area that is, as you say, worth exploring.


BERT (Devlin et al., 2018) fine-tuned on QQP achieves over 90% accuracy on QQP, but only 33% accuracy on PAWS data in the same domain.

in our experiments, we show the limitations of this modeling choice on PAWS pairs.


In 1953, the team also toured in Australia.

models that do not capture non-local contextual information fail even with PAWS training examples.


Table 1 summarizes the properties of these corpora.

transformer and LStM outperform all the other models in the highest and the lowest error-rated corpora, respectively.


The original ICNALE is not error annotated.

for example, the performance of the transformer ranges from the score of f 0.5 , which is as low as 36.20 on CoNLL-2013, to as high as 60.06 on JfLEG.


The following factors are considered while selecting our model.

single-corpus evaluation is deemed weak regardless of its diversity.


To reduce model complexity, we replace the fully-connected structure with a star-shaped topology, in which every two non-adjacent nodes are connected through a shared relay node.

the maximum dependency path length of Star-transformer is O(1) with a constant two via the relay node.


During training, the encoder and the classifier play a co-operative game, while the encoder and the discriminator play an adversarial game.

instead of classifying sentences independently with a Softmax layer, our second method is to model them jointly with a CRF layer (Lafferty et al., 2001).


This requirement is again shared by § 185 to § 187 StGB.

the independent classification shows already that the amount of data is insufficient.


a standard form available in a dictionary.

the results suggest that our method works well for detecting cyber security events from noisy short text.


A corpus of 6,000 tweets describing software vulnerabilities is annotated with authors' opinions toward their severity.


As such, the performance of all the considered models on this dataset is lower than that on the Twitter and Weibo datasets.

each feature type is processed by a feature branch with a number of fully-connected (FC) layers.


By integrating these hidden layers on top of a deep network, which produces the MRF potentials, we obtain our deep MRF model for fake news detection.

we select T = 5 as it produces the best trade-off between accuracy and computational complexity.


By sharing parameters, the networks regularize each other, and the network for one task can benefit from repre- sentations induced for the others.

gold Adv MTL LSTM Sentence (1) 5 5 5 7 But, star gazer, we had guns then when the Constitution was written and enshrined in the BOR and now incorporated into th 14th Civil Rights Amendment.


n}, and their average vector, s = 1/n i w i , s ∈ R d , as follows: Figures 1(a)-1(c) show strong positive correlation between divergence and document length show macro F1 performance of the deep averaging network developed in (Joulin et al., 2017) across datasets: performance considerably drops for higher values of information loss/divergence, e.g.

they can cause efficiency issues because of their large size (d × k); note that the typical value for embedding dimension d is 300 (Pennington et al., 2014;Mikolov et al., 2013).


although 'he is' was poor in offensive content in the trial dataset (15%), we kept it as a keyword in order to avoid gender bias, and we found that in the full dataset it was more offensive (32.4%).


It includes offensive tweets targeted at organizations, situations, events, etc., thus making it more challenging for models to learn discriminative properties for this class.

to the best of our knowledge, no prior work has explored the target of the offensive language, which might be important in many scenarios, e.g., when studying hate speech with respect to a specific target.


in order to show the effect of KB concept embeddings, we visualize the probabilities of word transcription to be predicted for each event type.


Micro  Table 5: Comparison with different traditional classifiers in AD:Control classification task.

next, we do two ablation studies on the arrangements of modalities.


As an example, a recent RE method called BRAN (Verga et al., 2018) proposed to use encoder of Transformer (Vaswani et al., 2017) for obtaining token representations and then used these representations for RE.

we experimented with different ablations of BRAN and noticed an improvement in results for DCN dataset upon removing multi-head selfattention layer.


The specifications of these source data sets are given in Table 1.

source data set Yelp is more similar to CADEC than PubMed and MIMIC from both PPL and WVV perspectives.


Results from all models outperform the baseline SVR model: Pearson's 2 http://nlp.stanford.edu/data/glove.

given the results presented so far mixing annotators may be beneficial given their respective trade-offs of precision and recall.


Our results, in fact, point to a future direction which applies the edit-tree formalism, but alleviates the edit-tree explosion by exploiting the relationships between the edit-tree classes potentially using representation learning methods.

12 The results of this experiment are reported in Table 5.


Comparing encoder representations to decoder representations, it is interesting to see that in several cases the decoder side representations performed better than the encoder side ones, even though the former were trained using a uni-directional LSTM.

we trained and evaluated the semantic and the syntactic classifiers on existing annotated corpora.


The transition is the multiplicative attention function with h (enc) and h (dec) j as input.

figure 3: We present performance (in accuracy) averaged over the 20 languages from UD we consider.


However, in the low-resource case, we would expect direct supervision on the sort of features we desire to extract to work better.

the gap between the performance of our model and Lematus-ch20 is larger when fewer training data are available, especially for ambiguous tokens.


Each individual word is denoted as w i .

the task is quite nuanced as the proper choice of the lemma is context dependent.


This is in line with previous work which shows that the linguistic nature of the target task is important when predicting typological features with language embeddings (Bjerva and Augenstein, 2018a).

unlike principles, parameters are the parts of linguistic structure that are allowed to vary.


Word clusters distinguish themselves from word embedding models by their ability to learn from little data (Bansal et al., 2014;Qu et al., 2015); for example, in cases like (Bansal et al., 2014), word clusters outperform other kinds of representations, including word embeddings.

word clusters differentiate themselves from word embeddings by requiring estimation of many fewer parameters, and by their ability to derive qualitative representations from smaller corpora (Qu et al., 2015;Bansal et al., 2014).


Since the vocabulary size is fixed, as the number of clusters k increases, purity can increase even if   Where M I and H stand for Mutual Information and Entropy, respectively.

measuring on the Universal Dependencies corpora, we find that the percentage of polyclass words (i.e.


In our experiments, purity measures the percentage of vocabulary that is labeled correctly.

for experiments on larger corpora, we use the unlabeled EuroParl corpus (Koehn, 2005).


In Figure 3, we show PoS purity for clusters induced over Universal Dependencies (UD) corpora, where we consider all polyclass words as clustered incorrectly.

a value of, say 0.3, does not indicate that 30% of the points have been properly separated.


though many variations have been proposed, the underlying structures of multilingual topic models are similar.


Interestingly, the H-distances of test documents are at a less ideal value, although they are slightly decreasing in most of the languages except AR.

multilingual topic models are generally extended from Latent Dirichlet Allocation (Blei et al., 2003, LDA).


This raises the question of whether features extracted with BiLSTMs in themselves capture information about subtrees, thus making recursive composition superfluous.

each time we attach a dependent to a head, we construct a vector which is a concatenation of h, d and r. We pass this vector to the LSTM of h. c is the output state of the LSTM after passing through that vector.


McDonald and Nivre (2007) and Mc-Donald and Nivre (2011) have shown that historybased features enhance transition-based parsers as long as they do not suffer from error propagation.

this can be explained by language properties: the right-headed languages suffer more from ablating the backward LStM than other languages.


Stack LSTMs are used to represent the stack, the buffer, and the sequence of past parsing actions performed for a configuration.

nivre (2006) has also shown that lookahead features are absolutely crucial given the greedy left-to-right parsing strategy.


In this work, we propose a method for density matching for bilingual word embedding (DeMa-BWE).

(2) A strong and robust unsupervised self-learning method SLunsup from (Artetxe et al., 2018b).


Layer 1 and 2 alignments are based on anchors from the first and second LSTM layer output respectively.

we emphasize that by constraining w s→t to be orthogonal, we also preserve relations between e i,c andê i,c that represent the contextual information.


4 is no longer well-defined, as there are many corresponding vectors to both the source and the target words.

4 Balto arrives , distracts the bear , saves Aleu , they both escape and the bear disappears .


2 As Table 1 shows, the shift of these words is indeed slightly higher than it is for other words.

we thank the MIT NLP group and the reviewers for their helpful discussion and comments.


Through reinforcement learning ERD is able to learn the minimum number of posts required to identify a rumour.

and lastly, CM is optimised by minimising the cost: We train CM and RDM in an alternating fashion, i.e.


In this paper, we follow a commonly accepted definition of rumour, that it is an unverified statement, circulating from person to person and pertaining to an object, event, or issue of public concern and it is circulating without known authority for its truthfulness at the current time, but it may turn out to be true, or partly or entirely false; alternatively, it may also remain unresolved (Peterson and Gist, 1951;Zubiaga et al., 2018).

our aim is to identify rumours as early as possible, while keeping a reasonable detection accuracy.


As expected, ICES contains neighbors of characters which are merely visually similar without representing the same underlying character (such as Λ as a neighbor of A, or ⅼ as a neighbor of i).

for instance, at p = 0.5, POS improves by about 20pp, while AT alone had an effect of only 12pp and the effect of CE was even negative.


For this, we compute the difference in the performance decrease normalized by the test performance on the clean data.

vELMo embeddings better capture similarity between visually similar words.


The compression c(x, y) becomes The challenge here is that choices are not made independently from another.

we first represent each word using two different embedding channels.


(2018) also examines diversity and quality (which they call precision and recall) in the context of generative image models.

proof The lower bound falls out of the definition of L * .


Specificity (WD, CT) For our weighted decoding models, the extreme settings (very generic and very specific) score poorly in engagingness due to the frequent presence of degenerate output -see Figure 3 (middle).

to fix this, we introduce an extra setting z = 10 (boost), in which we do not use the feature extrep bigram for weighted decoding during beam search, but we do use it to rerank the candidates after beam search.


We evaluate the effectiveness of both control methods via automatic metrics (i.e., measuring how well the attribute was controlled), and use our findings to select control methods and control settings to be explored further via human evaluation (Section 8).

the sentence embedding sent emb(s) for an utterance s is a Input: Do you go get coffee often Baseline Response: I do, when I am not playing the piano.


recently, energy based neural structured prediction models (Amos et al., 2016;Belanger and McCallum, 2016;Belanger et al., 2017) were proposed that define an energy function over candidate structured output space and use gradient based optimization to form predictions making the overall optimization search aware.


Since, the local normalizer is easy to compute, likelihood maximization based training is a standard approach for training these models.

we demonstrate the effect of both sources of label bias through our experiments on two common sequence tasks: CCG supertagging and machine translation.


Given sentence vector s i as input, SUMO computes: r i , e ij = TMT(r i ,ẽ ij ) (11) Iterative Structure Refinement SUMO essentially reduces summarization to a rooted-tree parsing problem.

the Road Home, the Louisiana grant program for homeowners who lost their houses to hurricanes Katrina and Rita, is expected to cost far more than the $7.5 billion provided by the Federal Government, in part because many more families have applied than officials had anticipated.


At the same time, they are noisy and may not reflect the desired phenomenon.

one aspect of controllability is intention; our model produces contrastive claims without understanding the view of the original claim.


when we combine this model with constrained decoding, the improvement is smaller than for the other settings.


We include the baseline, the baseline with constrained decoding, and the best constrained model ("COUNT + SUB + COPY") according to BLEU and partial match.

addressing point 2 is more difficult due to the variety of possible substitutions, including named entities.


Because of these two restrictions, ONTONOTES only has 195K markables, and a low markable density (0.12 markable/token).

we use PD silver to train a coreference system able to simultaneously identify non-referring expression and build coreference chains (including singletons).


Empirical study of argumentation requires examples drawn from authentic, human-authored text.

we have used our method to quickly and cheaply produce a large, argument-annotated data set of product reviews, which we freely release, along with the source code to our annotation interface and processing tools.


Human dialogs are like well-structured buildings, with words as the bricks, sentences as the floors, and topic transitions as the stairs connecting the whole building.

7(a) generated by HMM, even if we set 10 states in the HMM, some states are still collapsed by the model because they share a similar surface form.


The dialog system is controlled by a fixed structure and hand-set probabilities.

we also proposed a way to incorporate the learned dialog structure information into a downstream dialog system building task.


Caselli and Vossen (2017) showed that only 117 annotated causal relations in this dataset are indicated by explicit causal cue phrases while the others are implicit.

understanding causal relations between events in a document is an important step in text understanding and is beneficial to various NLP applications, such as information extraction, question answering and text summarization.


(2018) proposed to model the usefulness of reviews using review-level attention to enhance the learning of user and item representations.

for example, in the sentence "The laptop I bought yesterday is too heavy", the word "heavy" is more informative than the word "yesterday" in representing this laptop.


For example, if a user frequently mentions "the price is too high" and "very expensive" in his/her reviews for different items, then we can infer this user may be sensitive to price.

in addition, we incorporate a three-tier attention network into our model to select and attend to informative words, sentences and reviews to learn more accurate representations of users and items.


We propose an alternative approach that is based on matrix norms and which proved to be more noise-robust by focusing primarily on high word similarities.

the most popular method to come up with word vectors is Word2Vec, which is based on a 3 layer neural network architecture in which the word vectors are obtained as the weights of the hidden layer.


We experimented with different keyword list sizes but obtained the best results with rather few and therefore precise keywords.

it is quite striking that, although sn 1 lacks two properties of a normalized similarity measure (boundedness by 1 and symmetry), it reaches quite good results on contest 1.


Symmetry directly follows, if we can show that Z = Z for arbitrary matrices Z, since with this property we have Let M and N be arbitrary matrices such that MN and NM are both defined and quadratic, then (see (Chatelin, 1993)) where ρ(X) denotes the largest absolute eigenvalue of a squared matrix X.

this measure was evaluated on the task to assign users to the best matching marketing target groups.


The evaluation showed that the inter-annotator agreement values vary strongly for contest 2 part 2 (minimum average annotator agreement according to Cohen's kappa of 0.03 while the maximum is 0.149, see Table 4).

several of our matrix norm based similarity estimates focus primarily on strongly related word pairs and are therefore less vulnerable to noise.


Our complete pipeline comprises the following steps: 1.

to further explore this issue of whether normalization thus formed classification model has any benefit over GCN and GAt, we experiment with the Cora and Citeseer scientific document classification tasks (as reported in (Kipf and Welling, 2016)  table 3: Summary of the fine-grained F 1 @15 on Se-mEval.


Our approach is an alternative to this technique, providing an approximate max-violating inference with respect to AP for a more general case of the ranking represenation.

set, plotting the learning curves over the training epochs, T .


In their case, this is due to the impossibility of the global inference (their model is also augmented with the structural component describing item-item interactions) and the scale of the task (they do ranking for recommendation domain).

weston and Blitzer (2012) bypass the necessity of comparison to a complete ranking during training and sample the candidate pairs.


1) of the two ranking outputs, the current ground truth r * i and the max-violatingr i .

we start with the last N th position of the rank and put there the minimum weighted item: According to the decomposition in Eq.


The max number of epochs, T , is set to 100, for both LSP and LSP-AP.

the exact algorithm for max-violating inference of Yue et al.


(Mohammad et al., 2016) and related deep learning based methods including MITRE (SemEval-2016 best performing system) (Zarrella and Marsh, 2016), n-grams+embeddings (Mohammad et al., 2017), TGMN-CR (Wei et al., 2018b), T-PAN (Dey et al., 2018), AS-biGRU-CNN (Zhou et al., 2017), and TAN (Du et al., 2017).

most of the related work of tweet stance detection explored the traditional deep learning models in their methods.


By contrast, the WE WPI system specifically examines the difference between the word positions of the translation and reference, not the difference of lengths between the translation and reference.

the P and Q consist of some P i and Q j , which are the respective signatures.


The induced embeddings are evaluated in three tasks: bilingual lexicon induction (BLI), multilingual dependency parsing, and multilingual document classification.

furthermore,  empirically prove that the method typically fails for distant language pairs such as English-finnish.


Training stops when the perplexity on the development set has not improved for 20 checkpoints (1000 updates/batches per checkpoint).

nMT Setup Our nMT models are developed in Sockeye 5 (Hieber et al., 2017).


This is not surprising, as when there is enough in-domain data, continued training on only the in-domain data can already achieve a pretty good performance, and we do not need to use extra unlabeled-domain data to augment it any more, neither does curriculum learning.

the probabilistic curriculum (Bengio et al., 2009) works by dividing the training procedure into distinct phases.


In this work we present two primary methods of synthesizing natural noise, in accordance with the types of noise identified in prior work as naturally occurring in internet and social media based text (Eisenstein, 2013;Michel and Neubig, 2018).

similarly for emoticons, we randomly select an emoticon and insert it on both sides.


These represent small nuances which the model learns to capture with increasing supervision.

if we further fine-tune the model using only 10k MTNT data, we note that the model still struggles with generation of *very*.


For our baseline model we use the standard Transformer Base model (Vaswani et al., 2017).

we propose a novel n-gram level retrieval approach that relies on local phrase level similarities, allowing us to retrieve neighbors that are useful for translation even when overall sentence similarity is low.


In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous datasets and on sub-tasks like rare phrase translation.

we use a Transformer based decoder that attends to both, the encoder outputs and the CSTM, in every cross-attention layer.


A machine reading comprehension model is designed to predict the start and end positions in the article of the answer.

their method relies heavily on the multi-branch structures of the tasks, which are not widely applicable in neural machine translation.


Recent work tackled the training problem using variants of reinforcement learning (RL) (Suhr and Artzi, 2018;Liang et al., 2018) or maximum marginal likelihood (MML) (Guu et al., 2017;Goldman et al., 2018).

improving beam search has been investigated by proposing specialized objectives (Wiseman and Rush, 2016), stopping criteria , and using continuous relaxations (Goyal et al., 2018).


the split 'testA' contains object categories sampled randomly to be close to the original data distribution, while 'testB' contains objects sampled from the most frequent object categories, excluding categories such as 'sky', 'sand', 'floor', etc.


The best model is selected based on the validation loss after training for 50 epochs.

initial datasets, e.g., VQAv1 (Antol et al., 2015) and COCO-QA (Ren et al., 2015a), exhibited significant language bias in which many questions could be answered correctly without looking at the image, e.g., for VQAv1 it was possible to achieve 50% accuracy using language alone (Kafle and Kanan, 2016).


We pre-train a neural network model to accurately understand the advice.

as there are now significantly more regions.


model would have made a prediction ('x') close to the true block (square).

in the first step, the model from Section 2.5 self-generates restrictive advice based on the most confident predicted region, which it uses as input in the end-to-end model.


We gratefully acknowledge the donation of a GPU from the NVIDIA Grant Program.

we use a sequence-to-sequence model with an attention mechanism to map a textual description of a node to its path representation.


Furthermore, the proposed model is more interpretable for two reasons.

we use an LSTM (Hochreiter and Schmidhuber, 1997) encoder to project the textual description into a vector space and an LSTM decoder to predict the sequence of entities that are relevant to this definition.


We evaluate our ablation baselines on IQUAD V1 and EQA, reporting top-1 QA accuracy (Table 4) given gold standard navigation information as V. These decoupled QA models do not take in a previous action, so we do not consider A ONLY ablations for this task.

the agent sees more of the scene, but can take more training iterations to learn to move to the goal.


Furthermore, if we add LSTM span predictors along with the video LSTM (ExCL 2-{b, c}) we obtain an additional boost in performance.

we verify empirically that our method significantly outperforms prior work on two benchmark datasets -TACoS, ActivityNet and comparably well on the third, Charades-STA.


unimodal visual model for German verb sense disambiguation, but we find the opposite for Spanish unimodal verb sense disambiguation.

table 4 shows the results of the translation experiment.


There are two existing multimodal translation evaluation sets with ambiguous words: the Ambiguous COCO dataset (Elliott et al., 2017) contains sentences that are "possibly ambiguous", and the Multimodal Lexical Translation dataset is restricted to predicting single words instead of full sentences (Lala and Specia, 2018).

when visual information is added to textual features, models in both the languages predict the correct label.


For word-level tagging, we use a hierarchical bidirectional LSTM (BiLSTM) that incorporates both token-and character-level information (Plank et al., 2016), similar to the winning system (Samih et al., 2016) of the Second Code-Switching Shared Task (Molina et al., 2016).

we replace usernames with @username in order to preserve privacy.


In experiments on a new Spanish-Wixarika dataset and on an adapted German-Turkish dataset, our proposed model performs slightly better than or roughly on par with our best baseline, respectively.

intra-word CS was not handled explicitly, and often systems even failed to correctly assign the mixed label.


For freer word order languages such as Polish or Latin, we observe a substantial drop in performance because most information on inter-word relations and their roles (expressed by means of case system) is lost.

there, precision@10 is on average 8 points higher than precision@1.


We also observe that most uncertainty comes from morphological categories such as noun number, noun definiteness (which is expressed morphologically in Bulgarian), and verb tense, all of which are inherent (Booij, 1996) 8 and typically cannot be predicted from sentential context if they do not participate in agreement.

we also observe that most uncertainty comes from morphological categories such as noun number, noun definiteness (which is expressed morphologically in Bulgarian), and verb tense, all of which are inherent (Booij, 1996) 8 and typically cannot be predicted from sentential context if they do not participate in agreement.


This is within expectations, because with only L N M T , weights related to NSDs are kept to the initial values and were not updated, and hence detrimental to learning.

although using a shorter sequence may improve the efficiency, some syntactic information is lost.


In SS and DSS, the probability of using reference words s is annealed using inverse sigmoid decay : s = k/(k + exp(i/k)) at the i-th checkpoint with k = 10.

we train the models using the Adam optimizer (Kingma and Ba, 2015) with a batch size of 1024 words.


(2018) proposed to complete a predicted prefix with all possible reference suffixes and picking the reference suffix that yields the highest BLEU-1 score.

the schedule sampling hypothesis uses a mixture of the reference (black) and sampled (blue underlined) words, while the entire hypothesis sequence is sampled in our approach.


This mimics the gradual annealing of the online curriculum, so one possibility is that the agent is simply choosing the cleanest bin whenever it can, and its good performance comes from the enforced period of exploration.

figures 4, 5 and 6 show coarse representations of the policies learned by the Q-learning agent on the Paracrawl and WMT English-french datasets.


The former, by design, telescopes towards the clean bins.

the fact that the agent beats the fixed -schedule (see Table 1) described above on both corpora makes this unlikely.


At a high level, the regularization term keeps parameters which are important to general-domain performance close to the initial general-domain model values during continued training, while allowing parameters less important to general-domain performance to adapt more aggressively to the in-domain data.

the first, multi-objective fine-tuning, which they denote MCL, trains the network with a joint objective of standard loglikelihood loss plus a second term based on knowledge distillation (Hinton et al., 2015;Kim and Rush, 2016) of the general-domain model.


Continued training is an effective method for domain adaptation in neural machine translation.

(2017) found that mixing general-domain data with the in-domain data used for continued training improved generaldomain performance of the resulting models, at the expense of training time.


In all these cases, the meaning of the word stays the same, despite the change in context.


Our unsupervised approach (LP) is almost on a par with the most complex SVM.

their constituents are often not semantically related.


We observe that when enough data is available (e.g.

accuracy) that we intend to optimize cannot be observed.


The joint loss is the Figure 1: High-level overview of our proposed TL architecture.

uLMFiT trains a LM and fine-tunes it to the target dataset, before transferring it to a classification model.


An auxiliary task (next sentence prediction) is used to enhance the representations of the LM.

a comparison of the P-LM + aux model and the P-LM model shows that the performance of SiATL on classification tasks is improved by the auxiliary objective.


We hypothesize that this is due to two reasons.

we tokenize with the NLTK's TweetTokenizer (Bird, 2006), lowercase all text, and use regular expressions to remove stop words, numbers, urls, consecutive repeated words and Twitter users (i.e., tokens whose first character is '@').


multi-task learning and personalization did not contribute to the improvement of these auxiliary tasks.


An arguably more promising direction is to focus on fact-checking entire news outlets, which can be done in advance.

using multi-task ordinal regression is novel for these tasks, and it is also an under-explored direction in machine learning in general.


The input sentence w 1:n is encoded as a one-hot vector, v (total occurrence weighting scheme).

the amount of available fan fiction for this saga allows to create a large corpus.


For instance, ACL reviews tend to contain more propositions than those in ML venues, especially with more requests but fewer facts.

among the exactly matched proposition segments, we report a Cohen's κ of 0.64.


We draw our corpus from the public-domain texts on Project Gutenberg, selecting individual works of fiction (both novels and short stories) that include a mix of high literary style (e.g., Edith Wharton's Age of Innocence, James Joyce's Ulysses) and popular pulp fiction (e.g., H. Rider Haggard's King Solomon's Mines, Horatio Alger's Ragged Dick).

we expand the criteria for PER to include such characters who engage in dialogue or have reported internal monologue, regardless of their human status (this includes depicted non-human life forms in science fiction, such as aliens and robots, as well).


This alone-that cross-domain performance can be so strikingly worse-is a significant result, providing the first estimate of how performance degrades across these domains for this task.

geo-political entities are single units that contain a population, government, physical location, and political boundaries (LDC, 2005).


At the end of annotating, the inter-annotator agreement was calculated by double-annotating the same five texts and measuring the F1 score.

to existing datasets built primarily on news (focused on geopolitical entities and organizations), literary texts offer strikingly different distributions of entity categories, with much stronger emphasis on people and description of settings.


Previous research on automated abusive language detection in Twitter has shown that communitybased profiling of users is a promising technique for this task.

once the model is trained, we extract 200-dimensional embeddings E = A F W (1) from the first layer (i.e., the layer's output without activation).


(2017) who found reducing finegranularity of senses beneficial to some settings.

(2016) that the reverse dictionary system performs best with the bag-of-words (BoW) input encoding and the ranking loss.


(2016) which leverages a standard neural architecture in order to map dictionary definitions to representations of the words defined by those definitions.

in this paper, we provide an analysis to highlight the importance of addressing the meaning conflation deficiency.


It is widely acknowledged that sense distinctions in WordNet inventory are too fine-grained for most NLP applications (Hovy et al., 2013).

the sense representations used in our experiments (DeConf) were constructed by exploiting the knowledge encoded in WordNet.


Alternatively, senses can be automatically induced in an unsupervised manner by analyzing the diversity of contexts in which a word appears (Schütze, 1998;Reisinger and Mooney, 2010;Huang et al., 2012;Neelakantan et al., 2014;Guo et al., 2014;Šuster et al., 2016).

in our experiments we leveraged DeConf (Pilehvar and Collier, 2016).


Section 4), we suspect that there is an intrinsic limit to how well generating from an AMR graph can replicate the reference realisation.

given the inherent difficulty of predicting a single syntax realisation (cf.


OS: The Open-Sesame (Swayamdipta et al., 2017) classifier, pre-trained on the FN corpus (release 1.7).

as an example usage of our corpus, we used it to evaluate these frame disambiguation models: 1.


A Ambiguity Examples in the Corpus    # SENTENCE SQS FRAMES (F SS) 1 These writings lack the mystical, philosophical elements of alchemy, but do contain the works of Bolus of Mendes (or Pseudo-Democritus), which aligned these recipes with theoretical knowledge of astrology and the classical elements.

this test shows what is the best possible performance over our corpus that can be expected from a system such as OS that selects a single frame per sentence.


The triple captures how much interest a user puts on a document given a query.

2017, we use TransE as a strong baseline model for the search personalization task.


These two datasets are created to avoid reversible relation problems, thus the prediction task becomes more realistic and hence more challenging (Toutanova and Chen, 2015).

our MRR and Hits@1 scores are higher than those of TransE (with relative improvements of 14.5% and 22% over TransE, respectively).


This is because annotating complex structures typically require certain expertise, and smaller tasks are often easier (Fernandes and Brefeld, 2011).

the results shown here implies that the information theoretical benefit of partialness can possibly offset its disadvantages for learning.


This scenario has caused a challenge called multi-level answer ranking (Liu et al., 2018).

latent representation models aim to jointly learn lexical and semantic information from QA sentences and influence the vector generation directly, e.g., attention mechanism (Bahdanau et al., 2015).


Note that Model 8 leverages additional signals, including URL information, character-level encodings, and external term features such as tf-idf.

because our model yields different numbers of h i with queries of different lengths, further aggregation is needed to output a global feature v. We directly average all vectors v = 1 Nq h i as the aggregated feature, where N q is the length of the query.


The relationship between the PTDL curves in the exclusive evaluation shows that CNN-BiLSTM is actually the optimal tagging architecture for a brief window, overtaking CRF around 30K tokens and staying in front of BiLSTM-CRF until about 125K tokens.

active Learning active learning seeks to maximize the performance of a model while minimizing the manual annotation required to train it.


In practice, probability vectors of LMs tend to be sparse (Kim et al., 2016).

a commonly discussed drawback of such LM-based text generation is exposure bias (Ranzato et al., 2015): during training, the model predicts the next token conditioned on the ground truth history, while at test time prediction is based on predicted tokens, causing a train-test mismatch.


In IWGAN, a character level language model was developed based on adversarial training of a generator and a discriminator.

the output locus of the soft-GAN decoder would be two red line segments as depicted in Figure 2 (Right panel) instead of two points (in the one-hot case).


Text generation systems often generate their output from an intermediate semantic representation (Yao et al., 2012;Takase et al., 2016).

for such cases, machine learning techniques emulate human cognition and learn from training examples to predict future events.


For such cases, machine learning techniques emulate human linguistics and learn from training examples to predict future events.

we obtain an 11.6 BLEU improvement through semi-supervised training using the output of a grammar-based parser, compared to training on gold data only.


Further examples of the diversity of generation are given in the appendix.

due to language variability, the same plan entity may appear in several forms in the textual sentences.


(2018b) propose a framework for fine tuning using policy gradients and perform a human evaluation showing promising results.

we train the model using maximum likelihood before fine tuning.


Generated questions should be formed of language that is both fluent and relevant to the context and answer.

the generator is rewarded for successfully fooling the discriminator.


We collapse co-referential entities into a single node associated with the longest mention (on the assumption that these will be the most informative).

to determine the shortcomings of our model, we calculate rough error statistics over the outputs of the GraphWriter on the test set.


Our dataset consists of 40k paper titles and abstracts from the Semantic Scholar Corpus taken from the proceedings of 12 top AI conferences (Ammar et al., 2018).

they do not directly model the graph structure, relying on linearization and sequence encoding instead.


This is likely caused by the lower quality KE model due to the larger vocabulary in AmazonQA.

we carefully created 51 test questions which were used to filter out untrusted judgments and workers.


Table 1 shows the distribution of the various types of training instances.

it may not include information about the menu/times for the breakfast, credentials for the wifi, or the cancellation policy for a spa appointment at the hotel.


We start off by testing our stronger encoder (i.e., ELMo) in absence of edges connecting mentions in the supporting documents (i.e., us-  Table 3: Ablation study on WIKIHOP validation set.

for example, in figure 1, our model would be aware that "Stockholm" and "Sweden" appear in the same document but any context words, including the ones encoding relations (e.g., "is the capital of") will be hidden.


BERT-MRC has almost no improvement on restaurant, which indicates Wikipedia may have no knowledge about aspects of restaurant.

previous study (Xu et al., 2018a) has shown that incorporating domain word embeddings greatly improve the performance.


As a compromise, many online businesses leverage community question-answering (CQA) (McAuley and Yang, 2016) to crowdsource answers from existing customers.

we expand the vocabulary of the embedding layer from the pre-trained model on ReviewRC since reviews may have words that are rare in wikipedia and keep other hyper-parameters as their defaults.


In the exemplary question " Who wrote the book The Pillars of the Earth?

for example, falcon correctly annotates question from LC-QuAD: 'Name the military unit whose garrison is Arlington County, Virginia and command structure is United States Department of Defense' where expected entities are dbr:Arlington_County,_Virginia and dbr:United_States_Department_of_ Defense.


The reason behind the use of elastic search is its effectiveness over indexed KGs as reported by Dubey et al.

they mostly fail in case of short text (e.g.


This is mainly due to the fact that the rationales are not complete programs and fail to explicitly describe all important numbers and operations required to solve the problem.

the base model is referred to as "Seq2prog," while our model with categorization is "Seq2prog + cat."


The scale and diversity of this dataset makes it particularly suited for use in training deeplearning models to solve word problems.

using this representation language, our new dataset, MathQA, significantly enhances the AQuA dataset with fully-specified operational programs.


These changes are not always reflected in the rationales, leading to incorrect solutions.

there is a significant amount of unwanted noise in the dataset, including problems with incorrect solutions, problems that are unsolvable without brute-force enumeration of solutions, and rationales that contain few or none of the steps required to solve the corresponding problem.


We estimate artifacts by training the QANet model described in Section 5.2 on a version of DROP where either the question or the paragraph input representation vectors are zeroed out (question-only and paragraph-only, respectively).

at test time, we first determine this answer type greedily and then get the best answer from the selected type.


For example, the word "AlElm" 1 can accept many possible core-word diacritics depending on the intended meaning 1 In this paper, we use Buckwalter transliteration.

we approached the task as a sequence-to-sequence (seq2seq) problem ; taking advantage of the recent advancements in Neural Machine Translation (NMT) (Britz et al., 2017;Kuchaiev et al., 2018) among other applications where seq2seq models made a breakthrough (Yu et al., 2016;Witten et al., 2016;Abadi et al., 2016).


Despite those positive outcomes, several issues with multi-task learning for sequence labeling remain open.

actually, by training our model on NER, chunking and POS tagging, we report state-of-the-art (or highly competitive) results on each task, without using external knowledge (such as gazetteers that has been shown to be important for NER), or hand-picking tasks to combine.


The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources used to conduct this research.

this establishes a new state-ofthe-art on the test set, outperforming concurrently published work (Xiong et al., 2019) and matching the performance of a BERt model (Devlin et al., 2018) on this task.


Training data We train the relabeling function g on another synthetically-noised dataset D drop generated from the manually-labeled data D. To mimic the type distribution of the distantly-labeled examples, we take each example (s, m, t) and randomly drop each type with a fixed rate 0.7 independent of other types to produce a new type set t .

we show in the next section that this is not sufficient for denoising.


We generate artificial subjectverb agreement errors from large amounts of data.

both neural models obtain higher F 0.5 scores than the rule-based baseline, on average and across the board, i.e., +10.6 for LSTM ESL and +15.7 for LSTM ESL+Art .


We train these models using EM for 500 iterations or until convergence, and we select the model with the lowest perplexity from among 70 random restarts.

here we note a mixed result: whilst de, sv, and it do benefit from POS information, the other languages do not, obtaining great improvements from MUSE embed-dings instead.


This challenge should be taken into consideration in model design.

we also thank USC Plus Lab and UCLA-NLP group for discussion and comments.


Training on increasing amounts of target samples improves the model performances monotonically for each target language and the model leveraging the bilingual data consistently outperforms the monolingual baseline model.

in this section, we present our evaluation for zeroshot learning.


For both SVD-aligned and ADV-aligned, we use the embeddings as provided by the original authors.

among the 5 considered languages, Turkish seemed to benefit the least from cross-lingual learning in all experiments.


10 serves as a regularization that encourages the output of R(G) to be distinguishable among classes.

the performances of these works are highly dependent on manually annotated training data while annotation process is time-consuming and expensive.


Rotten Tomatoes and Idebate dataset (Wang and Ling, 2016) use online text as source, but they are relatively small in scale: 3.7K posts of Rotten-Tomatoes compared to 80K posts of TIFU-short as shown in Table 1.

second, we propose a novel abstractive summarization model named multilevel memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction.


Different from previous approaches, we propose to alleviate such bias issue by changing the source of summarization dataset.

high scores of the TIFU dataset in both metrics show that it is potentially an excellent benchmark for evaluation of abstractive summarization systems.


We collect data from Reddit, which is a discussion forum platform with a large number of subreddits on diverse topics and interests.

instead of using only the last layer output of CNNs, we exploit the outputs of multiple layers of CNNs to construct S sets of memories.


We combine the best variants from the three approaches into a single system by taking the majority vote from the models.

all content included is accurate, with no irrelevant details or repetitions.


In order to asses the effectiveness of AL for neural text compression we extend the OpenNMT 7 implementations with our interactive framework following Algorithm 1.

+ Coverage-AL: The urgency of the situation in Alaska , Defenders needs your immediate assistance .


It is therefore indispensable to minimize the cost of data annotation.

neural sequence-to-sequence (Seq2Seq) models have shown remarkable success in many areas of natural language processing and specifically in natural language generation tasks, including text compression (Rush et al., 2015;Filippova et al., 2015;Yu et al., 2018;Kamigaito et al., 2018).


Given a question, the system predicts an answer using an extractive summary as the source input.

these summaries should factually adhere to the content of the source text and present the reader with the key points therein.


Abstractive methods can thus introduce new words to the summary that are not present in the source article.

the articles were evenly split across the four competing systems, and each HIt was completed by 5 turkers.


As shown in Table 2, the linguistic output of SL & CL is closer to the language used by humans: Our agent is able to produce a much richer and less repetitive output than both BL and RL.

overall, the accuracy of the CL and RL models is close.


Language is highly abstract: one dialog can correctly describe a lot of different scenes in real world, so why should we force a dialog to fit one single example among them?

the full training procedure is specified in Algorithm 1.


we provide these results in Table 4 and observe that each component is indeed important for our model.


Intuitively, we expect our model to have a lower sample complexity since training our model involves learning fewer parameters.

for multimodal personality traits recognition on POM (left side of Table 1), our baseline is able to additionally outperform more complicated memory-based recurrent models such as MfN  on several metrics.


The models on the leaderboard are evaluated on a private unseen test set which contains 18 new environments.

while these tasks are driven by different goals, they all require agents that can perceive their surroundings, understand the goal (either presented visually or in language instructions), and act in a virtual environment.


Our speaker model is an enhanced version of the encoder-decoder model of Fried et al.

we introduce our environmental dropout method to mimic the "new" environment E , as described next in Sec.


Inspired by reading strategies, with limited resources and a pretrained transformer, we propose three strategies to improve machine reading comprehension.

we use a sigmoid function instead of softmax at the final layer ( Figure 1) and regard the task as a binary (i.e., correct or incorrect) classification problem over each (document, question, answer option) instance.


For experiments with two datasets, we use Algorithm 2; for experiments with three datasets we find the re-weighting mechanism in Section 4.2 to have a better performance (a detailed comparison will be presented in Section 5.4).

the similar gain indicates that our method is orthogonal to ELMo.


Since the GCN layer retains important structural information and is sensitive to positional data from the syntax tree, we consider it as a position-based approach.

in this paper, we introduced the application of GCN and attention mechanism to identification of verbal MWEs and finally proposed and tested a hybrid approach integrating both models.


Second, the attention-based variants further boosted performance in comparison with their counterparts without attention.

incorrect attention led to a large drop in segmentation accuracy.


Su and Lee (2017) also introduce a pixel-based model that learns character features from font images.

the final embedding of the target word is indirectly affected by the visual information.


The BPE algorithm constructs a subword list from raw data and lattice LSTM introduces subwords into character LSTM representation.

we examine their non-pretrained model performance for fair comparison.


Our work is in line with their work in directly using word information for CWS.

as shown in Table 5, among the ten most improved words, seven words are domain-specific noun entities, including person names, disease names and chemical compound names.


Although the in-word negative sampling method proposed above is expected to prevent our model from incorrectly splitting multi-character words, we still want our model to pay more attention to the segmentation of such words.

• On four datasets in different special domains, our model improves the word F-measure by more than 3.0%, compared with the state-ofthe-art baseline segmenter.


In contrast, we model characterbased POS tagging.

to further investigate the robustness of our model, we conduct experiments with different levels of corrupted tokenization in English.


While most pure character-level models cannot ensure consistent labels for each character of a token, our semi-CRF outputs correct segments in most cases (tokenization F 1 is 98.69%, see Table 4), and ensures a single label for all characters of a segment.

we calculate joint tokenization and UPOS (universal POS) F 1 scores.


Using a dictionary with NN is also popular (Zhang et al., 2018b;.


Our unsupervised dynamic speaker model differs from previous work in that we build speaker embeddings as a weighted combination of latent modes with weights computed based on the utterance.

second, we use the learned dynamic speaker embed- dings in two representative tasks in dialogs: predicting user topic decisions in socialbot dialogs, and classifying dialog acts in human-human dialogs.


Figure 1 shows a visual comparison between outputs generated by the two models.

to contextualize these results, we compare disfluency removal as a post-processing step after end-to-end speech translation with the original disfluent pardev test Model 1Ref 2Ref 1Ref 2Ref Postproc.


While SACNN can focus on important segments and gain local features, DepRNN helps to handle long-distance dependency between two entities based on the SDP as well as provide subject and object roles of two entities for the directional relation.

in this paper, we present a new model combining both CNNs and RNNs, exploiting the information from both the raw sequence and the SDP.


We also propose the SACNN which automatically focus on the essential segments and gains local features.

the relation classification task is treated as a multi-class classification problem.


In the future work, we will consider to detect events and their sentence-level and documentlevel factuality with a joint framework, and we will also continue to expand the scale of our DLEF corpus.

(Time: November, 2017) (Document-level factuality of the event "reach" is CT-.)


Table 2 indicates that sentence-level factuality usually agrees with document-level factuality in CT+ documents, making them straightforward to be identified.

the results is given in table 6, which shows that contexts can improve the performance more significantly on the Chinese corpus than the English corpus.


To our best knowledge, this is the first document-level event factuality corpus.

no previous work annotated a document-level corpus.


4, where Mintz (Mintz et al., 2009), MultiR (Hoffmann et al., 2011) and MIMLRE (Surdeanu et al., 2012) are conventional feature-based methods, and (Lin et al., 2016) and  are PCNN-based ones 4 .

table 5 compares the AUC values reported in these two papers and the results of our proposed models.


During the training, the model learns a joint network including F, E and D to minimize the empirical loss Eq (1).

in order to have a reasonable comparisons, we report the average ranking score for each method.


In order to transfer the rules into a new policy π r , the KL divergence between the posterior of π and π r should be minimized, this can be formally defined as minKL(P π (A t |S t , θ π )||P πr (A t |S t , θ π )) (5) Optimizing the constrained convex problem defined by Eq.

here we use a rule pattern as the Fig.1 shows (?).


• We apply the PR REINFORCE to the instance selection task for DS dataset to alleviate the wrong label problem in DS.

unbiased methods, such as REINFORCE, could usually take much time to train.


The Metropolis Hastings Walker (MHW) method (Li et al., 2014) scales well in the number of topics, and uses a collapsed inference algorithm, but it operates in the batch setting, so it is not scalable to large corpora.

the SCVB0 algorithm does not leverage sparsity, and hence requires O(K) operations per word token.


(S3) W AE W +W AE L +Sim: It is similar to W AE W +Sim except we also include the average embeddings of attribute labels associated with the instance.

we want the words and their attribute labels to be close to each other in the embedding space and the embeddings of different labels to be far away from each other.


Previous studies have mostly focused on estimating each annotator's overall reliability on the entire annotation task.

when constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators.


The function H denotes the cross entropy between two distributions.

the review representation is obtained by averaging word embeddings.


To solve the FDKB task using KBR, one feasible way is to exhaustively calculate the scores of all (r, t) combinations for the given head entity h. Afterwards, the highly-scored facts are returned as results.

semantic matching models such as REsCAL (Nickel et al., 2011), DistMult (Yang et al., 2014), Complex (Trouillon et al., 2016), HolE (Nickel et al., 2016) and ANALOGY (Liu et al., 2017) model the score of triples by the semantic similarity.


RE has been widely studied in NLP community for many years.

we aim to address them and further extensions of our model in future works.


Moreover, it is hard for machines to learn the attention weights from a long sequence of input text.

our RbSP model yields an F1-score of 86.3%, outperforms other comparative models, except Multi-Att-CNN model of  with multi-level attention CNN.


Our idea is based on fundamental notion that the syntactic structure of a sentence consists of binary asymmetrical relations between words (Nivre, 2005).

we propose a novel DNN framework which combines Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) and Convolutional Neural Networks (CNN) (LeCun et al., 1989) with a multi-attention layer.


Given questions in natural language (NL), the goal of KBQA is to automatically find answers from the underlying KB, which provides a more natural and intuitive way to access the vast underlying knowledge resources.

for instance, the same question can be expressed in various ways in NL while a KB usually has a canonical lexicon.


Unlike a basic memory network (Weston et al., 2014), its addressing stage is based on the key memory while the reading stage uses the value memory, which gives greater flexibility to encode prior knowledge via functionality separation.

4 shows the attention heatmap generated for a test question "who did location surrender to in number " (where "location" and " number " are entity types which replace the topic entity mention "France" and the constraint entity mention "ww2", respectively in the original question).


Following previous work , we also try building entailment-like training data from SQuAD 2.0 (Rajpurkar et al., 2018).

molecular and quantum physics show that the electromagnetic force is the fundamental interaction responsible for contact forces.


Preliminary experiments found that simply transferring the lower-level weights of extractive QA models was ineffective, so we instead consider three methods of con-structing entailment-like data from extractive QA data.

in this paper, we gather a large number of additional yes/no questions in order to construct a dedicated yes/no QA dataset.


Classify: Finally we feed [v * ; p * ] into a fully connected layer, and then through a softmax layer to predict the output class.

part of the value of this dataset is that it contains questions that people genuinely want to answer.


kVQU+AR: applies the approach introduced in this paper that additionally considers both the key and Value representations in the Query Updating (kVQU).


Then with the hop size increasing, the performance drops.

[2016] achieves the state-  Table 2: Running examples of addressed keys and corresponding relevance probabilities before and after introducing the STOP strategy.


We perform this additional contextualization only when sentences form contiguous text.

given a premise sentence P i , the entailment function f e computes a single hypothesis-aware representation x i containing information in the premise that is relevant to entailing the answer hypothesis H qa .


Models such as Decomposable Attention (Parikh et al., 2016) and ESIM (Chen et al., 2017), on the other hand, find alignments between the hypothesis and premise words through crossattention.

so we haveX This is similar to a standard attention mechanism, where attended representation is computed by summing the scaled representations.


To understand why, consider the set of premises in Figure 1, which entail the hypothesis H c .


The goal of this module is to identify sentences in the paragraph that are important for the given hypothesis.

such scaled addition is not possible when the outputs from lower layers are not of the same shapes, as in the following case.


Processed sentence after this step is sentence 2 in Figure 3.

since we do not have labeled data, we need to identify mentions in contexts, and assign gender labels to them.


Gendered Language Gendered language is the use of words and phrases that discriminate 1 the gender of a subject.

we also look at classifier probability distribution for human decisions shown in box and whisker plot in Figure 5, where x-axis is the classifier probability of the mention being female.


o t is the output and e t is the hidden state of the GRU.

so we first try to tackle it based on the sequence-to-sequence model, which is commonly used in machine translation.


This means the Seq2Seq model can better explain the hate symbols when Twitter users intentionally misspell or abbreviate common slur terms.


Distant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently.

evaluations P@100 P@200 P@300 Automatic@NYT 76.2 73.1 67.4 Manual@NYT 96.0(+19.8) 95.5(+22.4) 91.0(+23.6) Automatic@A-NYT 93.0 89.5 88.0 Manual@A-NYT 96.0(+3.0) 92.5(+3.0) 90.7(+2.7)  To further demonstrate the effectiveness of our training strategies, we compare Generative Adversarial Training (GAT) with other baselines on the partially labeled dataset A-NYT as shown in Figure 4(b).


To further alleviate the effect of wrong labeling problem, soft-label training algorithm (Liu et al., 2017b), reinforcement learning methods (Feng et al., 2018;Qin et al., 2018b) and additional side information (Vashishth et al., 2018;Wang et al., 2018) have been used.

a former negative instance has a big chance to be credible negative if any of its entities is not mentioned in the description of the other one.


The edges are weighted by the coreference and relation scores, which are trained according to the neural architecture explained in Section 3.1.

our Model We develop a general information extraction framework (DYGIE) to identify and classify entities, relations, and coreference in a multi-task setup.


3 This experiment envisions a pipeline where the noisy source is first automatically corrected and then translated.

combining this simple method with an automatic grammar correction system, we find that we can recover 1.5 BLEU.


(2017) employ a common encoder to encode the sentences from both the in-domain and out-of-domain data and meanwhile add a discriminator to the encoder to make sure that only domain-invariant information is transferred to the decoder.

in addition to the common encoder, Zeng et al.


In this paper, we present a method to make use of out-of-domain data to help in-domain translation.

our method can achieve a mild improvement on the out-of-domain compared to the baseline system.


For Encoder Context integration, the HAN encoder (Miculicich et al., 2018) is the best for TED and News datasets, however, the results are statistically insignificant with respect to our best model.

the Hierarchical Attention module has four operations: 1.


This amounts to 1,200 sentence pairs in the target side.

* " indicates that the correlation is significantly better than the next-best one.


This makes it possible to evaluate the effectiveness of adversarial attacks or defenses either using goldstandard human evaluation, or approximations that can be calculated without human intervention.

for a word-based translation model M 6 , and given an input sentence w 1 , .


The implementation of our method is available at https: //github.com/hassyGo/NLG-RL.

this section discusses our main contribution: how efficient our method is in accelerating reinforcement learning for sentence generation.


For reference, we report the test set results in Table 4.

for the En-Ja (2M) and En-Ja (2M, SW) datasets, we used a single GPU of NVIDIA Tesla V100 4 to speedup our experiments.


Better generation of rare words These BLEU scores suggest that our method for reinforcement learning has the potential to outperform the full softmax baseline.

we then review how reinforcement learning is used, and present a simple and efficient method to accelerate the training.


This paper has presented how to accelerate reinforcement learning for sentence generation tasks by reducing large action spaces.

e(y t ) is the y t -th row vector in W p , and the technique has shown to be effective in machine translation (Hashimoto and Tsuruoka, 2017) and text summarization (Paulus et al., 2018).


The work related to this paper falls into two sub topics, described as follows.

gal and ghahramani used dropout in DNNs as an approximate Bayesian inference in deep gaussian processes (gal and ghahramani, 2016) to mitigate the problem of representing uncertainty in deep learning without sacrificing the computational complexity.


Dropout-based methods have also been extended to various tasks such as computer vision , autonomous vehicle safety (McAllister et al., 2017) and medical decision making (van der Westhuizen and Lasenby, 2017).

we used review data from the Sports and outdoors category, with 272,630 data samples and rating labels from 1 to 5.


The Dropout operation will be randomly applied to the activations during the training and uncertainty measurement phrases, but will not be applied to the evaluation phrase.

the shortened intra-class distance and enlarged inter-class distance can reduce the prediction variance and increase the confidence for the accurate predictions.


We experiment with the following state-of-the-art neural text classification methods: 1.

helpfulness might be conflated with other reasons such as humour, sentiment in certain domains.


As a consequence the model has little freedom in discovering and concentrating on some natural label order.

• Sequence Generation Model (SGM) (Yang et al., 2018) which trains the RNN model similar to seq2seq-RNN but uses a new decoder structure that computes a weighted global embedding based on all labels as opposed to just the top one at each timestep.


Between the above two extremes are Vinyals-RNN-max and set-RNN (we have omitted Vinyals-RNN-sample and Vinyals-RNN-maxdirect here as they are similar to Vinyals-RNNmax).

if for each document, RNN finds one good way of ordering relevant labels (such as hierarchically) and allocates most of the probability mass to the sequence in that order, the model still assigns low probabilities to the ground truth label sets and will be penalized heavily.


For both lexicons, we keep 90% of the lexicon in train set and remaining 10% in validation set.

with this motivation, we focus on increasing accuracy on the most frequent words.


Figure 2 and Table 5 categorize the errors of systems trained on 3 types of lexicons (Romanized version of our lexicon is discussed in section 6.3) by using Algorithm 2.

for the remaining 30K words (29105 words to be exact), we observe that at least one set provides different transcription.


(2018) utilized additional side information from KBs for improved RE.

to the best of our knowledge, ours is the first principled framework to combine and jointly learn heterogeneous representations from both language and knowledge for the RE task.


All above models are based on handcrafted feature.

p φ (y|x) is calculated by the relation classifier from the semi-supervised learning framework.


In this paper, we proposed RCEND to fully exploit valid information of the noisy data in distant supervision relation classification.

sentence Ds Gold s1:Al Gore was waiting to board a commercial flight from Nashville to Miami... s2:There were also performers who were born in Louisiana , including Lucinda Williams... s3:Boggs was married, had three young children and lived in Brewster NA LivedIn it suffers from noisy labeling problem due to the irrelevance of aligned text and incompleteness of KB, which consists of false positives and false negatives.


FastText optimizes the loss function in Eq.1, but uses the scoring function s F T defined in Eq.2.

when the amount of misspellings is higher, i.e., r ∈ {0.25, 0.375}, MOE improves the results over the baseline for all of the α values.


In fact, it is the only component of the loss function which attempts to learn these relationships.

, w i+l } for some l set as a hyperparameter.